cancel
Showing results for 
Show  only  | Search instead for 
Did you mean: 
  • Don’t miss your chance to experience Discovery Summit Europe at our best available rate! Early bird registration through 31 Oct.
  • Image to Table JMP extension imports single or batch images as data tables. Useful to extract curves from pictures or convert image data to curve data to use in analyses like Functional Data Explorer. Download and install the extension.

Discussions

Solve problems, and share tips and tricks with other JMP users.
Choose Language Hide Translation Bar
xiaoyaj0
Level III

unique variance for each variable in multiple regression

In the attached dataset and screenshot, I am trying to find the effect of prior academic achievement and SES on current achievement for students. How could I find the unique variance/semipartial correlation for each variance? I know it is not the square of standard beta for the variable. Any ideas? Thank you!


1 ACCEPTED SOLUTION

Accepted Solutions
jiancao
Staff

Re: unique variance for each variable in multiple regression

JMP doesn't report semipartial correlations directly, but they can be calculated from regressions.

For example,  to get the semipartial correlation for socioeconomic status, first, run two regressions as shown in the screenshots.

12954_pastedImage_0.png12955_pastedImage_1.png

Then take the sqrt of the difference in RSquare values between the two models, i..e. sqrt(0.723934-0.715378)=0.0925. This indicates the "unique" contribution of socioeconomic status as a predictor.

View solution in original post

1 REPLY 1
jiancao
Staff

Re: unique variance for each variable in multiple regression

JMP doesn't report semipartial correlations directly, but they can be calculated from regressions.

For example,  to get the semipartial correlation for socioeconomic status, first, run two regressions as shown in the screenshots.

12954_pastedImage_0.png12955_pastedImage_1.png

Then take the sqrt of the difference in RSquare values between the two models, i..e. sqrt(0.723934-0.715378)=0.0925. This indicates the "unique" contribution of socioeconomic status as a predictor.

Recommended Articles